UIMA SDK is a software architecture and framework for supporting the development, integration, and deployment of search and analysis technologies. It can be used to analyze large volumes of unstructured information (text, audio, video, images, etc.) to discover, organize, and deliver relevant knowledge to the client or application end user.

Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.

uimaFIT provides Java annotations for describing UIMA components which can be used to directly describe the UIMA components in Java code without the need for traditional UIMA XML descriptors. This greatly simplifies refactoring a component definition (e.g., changing a configuration parameter name). uimaFIT also makes it easy to instantiate UIMA components without using XML descriptor files by providing convenient factory methods. This makes uimaFIT an ideal library for testing UIMA components because the component can be easily instantiated and invoked without requiring a descriptor file to be created first. uimaFIT is very useful in research environments in which programmatic/dynamic instantiation of UIMA pipelines can simplify experimentation. For example, when performing 10-fold cross-validation across a number of experimental conditions, it can be quite laborious to create a different set of descriptor files for each run, or even a script which generates such descriptor files. uimaFIT is type system agnostic and does not depend on (or provide) a specific type system.
This project has been superseded by the Apache uimaFIT project.

DKPro Core is a collection of software components for natural language processing (NLP) based on the Apache UIMA framework. Many powerful and state-of-the-art NLP components are already freely available in the NLP research community. New and improved components are being developed and released continuously. The components cover the whole range of NLP-related processing tasks. DKPro Core provides wrappers for such third-party tool as well as original NLP components. DKPro Core builds heavily on uimaFIT which allows for rapid and easy development of NLP processing pipelines.

Apache uimaFIT provides Java annotations for describing UIMA components which can be used to directly describe the UIMA components in Java code without the need for traditional UIMA XML descriptors. This greatly simplifies refactoring a component definition (e.g., changing a configuration parameter name). It also makes it easy to instantiate UIMA components without using XML descriptor files by providing convenient factory methods. It is ideal for testing UIMA components because the component can be easily instantiated and invoked without requiring a descriptor file to be created first.

DKPro WSD provides UIMA components which encapsulate corpus readers, linguistic annotators, lexical semantic resources, WSD algorithms, and evaluation and reporting tools. You configure the components, or write new ones, and arrange them into a data processing pipeline. DKPro WSD is modular and flexible. Components which provide the same functionality can be freely swapped. You can easily run the same algorithm on different data sets, or test several different algorithms on the same data set.

DKPro Lab is a lightweight framework for parameter sweeping experiments. It allows the user to set up experiments consisting of multiple interdependent tasks in a declarative manner with minimal overhead. Parameters are injected into tasks using via annotated class fields. Data produced by a task for any particular parameter configuration is stored and re-used whenever possible to avoid needless recalculation of results. Reports can be attached to each task to post-process the experimental results and present them in a convenient manner (e.g., as tables or charts).

DKPro Similarity is a framework for text similarity. Its goal is to provide a comprehensive repository of text similarity measures which are implemented using standardized interfaces. The framework is designed to complement DKPro Core, a collection of software components for natural language processing (NLP) based on the Apache UIMA framework. DKPro Similarity comprises a wide variety of measures ranging from ones based on simple n-grams and common subsequences to high-dimensional vector comparisons and structural, stylistic, and phonetic measures. In order to promote the reproducibility of experimental results and to provide reliable, permanent experimental conditions for future studies, DKPro Similarity also comes with a set of full-featured experimental setups which can be run out-of-the-box and used for future systems to built upon.